IDEAS: A parameter identification toolbox with symbolic analysis of uncertainty and its application to biological modelling

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 15, Issue PART 1, 2009, Pages 1271-1276

  Munoz Tamayo R ^a,b,c   Laroche, B ^a   Leclerc, M ^b   Walter, E ^a  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b CEMAGREF   (France)

^c UR341 National Agronomical Research Institute (INRA)   (France)

Author keywords

Parameter estimation; Sensitivity functions; Software; Symbolic differentiation; Uncertainty

Indexed keywords

BIOLOGICAL MODELLING; BIOLOGICAL MODELS; MATLAB TOOLBOXES; PRACTICAL IDENTIFIABILITY; SENSITIVITY FUNCTIONS; SYMBOLIC ANALYSIS; SYMBOLIC COMPUTATION; SYMBOLIC DIFFERENTIATION; UNCERTAINTY;

IDENTIFICATION (CONTROL SYSTEMS); PARAMETER ESTIMATION; SENSITIVITY ANALYSIS;

UNCERTAINTY ANALYSIS;

EID: 80051615768     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090706-3-FR-2004.0211     Document Type: Conference Paper

References (17)

1. Y. Cao, S. Li, P. Linda, and S. Radu. Adjoint sensitivity analysis for differential-algebraic equations: the adjoint DAE system and its numerical solution. SIAM Journal on Scientific Computing, 24(3):1076-1089, 2003.
2. N. Checchi, E. Giusti, and S. Marsili-Libelli. PEAS: A toolbox to assess the accuracy of estimated parameters in environmental models. Environmental Modelling & Software, 22(6):899-913, 2007. (Pubitemid 46186105)
3. D. Dochain and P. Vanrolleghem. Dynamical Modelling and Estimation in Wastewater Treatment Processes. IWA Publishing, London, 2001.
4. G. C. Goodwin and R. L. Payne. Dynamic System Identification: Experiment Design and Data Analysis. Academic Press, New York, 1977.
5. A. C. Hindmarsh and R. Serban. User documentation for cvodes v2.5.0, 2006. URL http://www.llnl.gov/CASC/sundials/.
6. S. Hoops, S. Sahle, R. Gauges, C. Lee, J. Pahle, N. Simus, M. Singhal, L. Xu, P. Mendes, and U. Kummer. COPASI-a COmplex PAthway simulator. Bioinformatics, 22(24):3067-3074, 2006.
7. R. W. Klopfenstein. Numerical differentiation formulas for stiff systems of ordinary differential equations. RCA Review, 32:447-462, 1971.
8. T. Maiwald and J. Timmer. Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics, 24(18):2037-2043, 2008.
9. R. Muñoz-Tamayo, J. P. Steyer, B. Laroche, and M. Leclerc. Human colon: a complex bioreactor. Conceptual modelling for the anaerobic digestion of the functional trophic chain. In Proc. 11th World Congress Anaerobic Digestion Bio-energy for our Future. Brisbane, Australia. 6 pages on CD-Rom, 2007.
10. R. Muñoz-Tamayo, B. Laroche, M. Leclerc, and E.Walter. Modelling and identification of in vitro homoacetogenesis by human-colon bacteria. In Proc. 16th IEEE Mediterranean Conference on Control and Automation. Ajaccio, France, pages 1717-1722, 2008.
11. K. Schittkowski. Numerical data fitting in dynamical systems. Kluwer Academic Publishers, 2002.
12. H. Schmidt. Sbaddon: high performance simulation for the systems biology toolbox for MATLAB. Bioinformatics, 23(5):646-647, 2007. (Pubitemid 46522605)
13. H. Schmidt and M. Jirstrand. Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics, 22(4):514-515, 2006. (Pubitemid 43231434)
14. W. Squire and G. Trapp. Using complex variables to estimate derivatives of real functions. SIAM Review, 40(1):110-112, 1998. (Pubitemid 128628446)
15. E. Walter and L. Pronzato. Identification of Parametric Models from Experimental Data. Springer, London, 1997.
16. Z. Zi and E. Klipp. SBML-PET: a systems biology markup language-based parameter estimation tool. Bioinformatics, 22(21):2704-2705, 2006. (Pubitemid 44642616)
17. Z. Zi, Y. Zheng, A. E. Rundell, and E. Klipp. SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool. BMC Bioinformatics, 9:342(14 pages), 2008.